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. 2022 Jul 4;19(7):2040-2047.
doi: 10.1021/acs.molpharmaceut.2c00112. Epub 2022 May 24.

iBCS: 2. Mechanistic Modeling of Pulmonary Availability of Inhaled Drugs versus Critical Product Attributes

Per Bäckman  1 , Antonio Cabal  2 , Andy Clark  3 , Carsten Ehrhardt  4 , Ben Forbes  5 , Jayne Hastedt  6 , Anthony Hickey  7   8 , Guenther Hochhaus  9 , Wenlei Jiang  10 , Stavros Kassinos  11 , Philip J Kuehl  12 , David Prime  13 , Yoen-Ju Son  14 , Simon P Teague  15 , Ulrika Tehler  16 , Jennifer Wylie  17
Affiliations

iBCS: 2. Mechanistic Modeling of Pulmonary Availability of Inhaled Drugs versus Critical Product Attributes

Per Bäckman et al. Mol Pharm. .

Abstract

This work is the second in a series of publications outlining the fundamental principles and proposed design of a biopharmaceutics classifications system for inhaled drugs and drug products (the iBCS). Here, a mechanistic computer-based model has been used to explore the sensitivity of the primary biopharmaceutics functional output parameters: (i) pulmonary fraction dose absorbed (Fabs) and (ii) drug half-life in lumen (t1/2) to biopharmaceutics-relevant input attributes including dose number (Do) and effective permeability (Peff). Results show the nonlinear sensitivity of primary functional outputs to variations in these attributes. Drugs with Do < 1 and Peff > 1 × 10-6 cm/s show rapid (t1/2 < 20 min) and complete (Fabs > 85%) absorption from lung lumen into lung tissue. At Do > 1, dissolution becomes a critical drug product attribute and Fabs becomes dependent on regional lung deposition. The input attributes used here, Do and Peff, thus enabled the classification of inhaled drugs into parameter spaces with distinctly different biopharmaceutic risks. The implications of these findings with respect to the design of an inhalation-based biopharmaceutics classification system (iBCS) and to the need for experimental methodologies to classify drugs need to be further explored.

Keywords: biopharmaceutics classification system; critical product attributes; iBCS; inhaled drugs; mechanistic modeling; pulmonary availability.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Schematic of the Mimetikos Preludium simulation model; fe = fraction exhaled, ET = mouth-throat, BB = tracheobronchial, Bb = bronchiolar, AI = alveolar interstitial, GI = gastrointestinal, C = central circulation, P = peripheral compartments, E = eliminated, T = transit compartments in GI, deep = deep compartment, Diss = dissolution of solid particles in lumen (ELF), Perm = permeation of dissolved compound over epithelium to/from lung tissue, Perf = exchange of compound between systemic circulation and lung tissue by perfusing blood, Kc = 1st-order rate constant for mucociliary transport from bb to BB (Kcbb) and from BB to 1st transit compartment (KcBB), distributional clearance to/from deep compartment, K = 1st-order rate constant, f = fraction, F = oral bioavailability, e = expectoration/nose drip, tr and in = intercompartmental transport in GI, a = absorption from ultimate GI compartment (Tn-T2n), xx = compartment number. The solid blue box encloses processes relevant for this study.
Figure 2
Figure 2
Response of calculated functional outputs luminal drug half-life (t1/2,j) (A, C, E) and the fraction of luminal dose absorbed (Fabs,j) (B, D, F) to variation in dose numbers (Do,j) and permeability (Peff) for the alveolar interstitial region (AI), (A, B), conducting airways (Bb, C, D), and total lung (E, F). Data for Peff = 1 × 10–5 cm/s (triangles); Peff = 1 × 10–6 cm/s (squares), and Peff = 1 × 10–7 cm/s (circles) are shown as separate graphs in each figure. Each datapoint is the average ± SD calculated for each combination of dose and solubility resulting in a given Do. For total lung (E, F), the average ± SD also includes a variation in the Bb/AI ratio from 2:8 to 4:6 at each Do.
See this image and copyright information in PMC

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